Systems and methods for progressive learning for machine-learned models to optimize training speed

ABSTRACT

Systems and methods of the present disclosure can include a computer-implemented method for efficient machine-learned model training. The method can include obtaining a plurality of training samples for a machine-learned model. The method can include, for one or more first training iterations, training, based at least in part on a first regularization magnitude configured to control a relative effect of one or more regularization techniques, the machine-learned model using one or more respective first training samples of the plurality of training samples. The method can include, for one or more second training iterations, training, based at least in part on a second regularization magnitude greater than the first regularization magnitude, the machine-learned model using one or more respective second training samples of the plurality of training samples.

RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalPatent Application No. 63/145,830. U.S. Provisional Patent ApplicationNo. 63/145,830 is hereby incorporated by reference in its entirety.

FIELD

The present disclosure relates generally to progressive learning ofmachine-learned models. More particularly, the present disclosurerelates to progressive adjustment of regularization during training of amachine-learned model to optimize training speed.

BACKGROUND

Recent advancements in machine learning have substantially increased thesize and complexity of both machine-learned models (e.g., neuralnetworks, etc.) and the data used to train them. As an example, thetraining of state-of-the-art deep learning models can sometimesnecessitate the utilization of thousands of graphics processing unitsfor weeks at a time, therefore presenting a prohibitively expensivecomputational cost. Other networks may train quickly but come withexpensive overhead as regards a large number of parameters As such, amethod that increases training speed and parameter efficiency wouldsubstantially increase the availability of computational resources forother tasks.

SUMMARY

Aspects and advantages of embodiments of the present disclosure will beset forth in part in the following description, or can be learned fromthe description, or can be learned through practice of the embodiments.

One example aspect of the present disclosure is directed to acomputer-implemented method for efficient machine-learned modeltraining. The method can include obtaining, by a computing systemcomprising one or more computing devices, a plurality of trainingsamples for a machine-learned model. The method can include, for one ormore first training iterations, training, by the computing system basedat least in part on a first regularization magnitude configured tocontrol a relative effect of one or more regularization techniques, themachine-learned model using one or more respective first trainingsamples of the plurality of training samples. The method can include,for one or more second training iterations, training, by the computingsystem based at least in part on a second regularization magnitudegreater than the first regularization magnitude, the machine-learnedmodel using one or more respective second training samples of theplurality of training samples.

Another example aspect of the present disclosure is directed to acomputing system for determination of models with optimized trainingspeed. The computing system can include one or more processors. Thecomputing system can include one or more tangible, non-transitorycomputer readable media storing computer-readable instructions that whenexecuted by the one or more processors cause the one or more processorsto perform operations. The operations can include generating a firstmachine-learned model from a defined model search space, wherein thedefined model search space comprises one or more searchable parameters,wherein the first machine-learned model comprises a one or more firstvalues for the one or more searchable parameters. The operations caninclude performing a model training process on the first machine-learnedmodel to obtain first training data descriptive of a first trainingspeed. The operations can include generating a second machine-learnedmodel from the defined model search space based at least in part on thefirst training data, wherein the second machine-learned model comprisesone or more second values for the one or more searchable parameters,wherein at least one of the one or more second values is different thanthe one or more first values. The operations can include performing themodel training process on the second machine-learned model to obtainsecond training data descriptive of a second training speed, wherein thesecond training speed is faster than the first training speed.

Another example aspect of the present disclosure is directed to one ormore tangible, non-transitory computer readable media storingcomputer-readable instructions that when executed by the one or moreprocessors cause the one or more processors to perform operations. Theoperations can include generating a first machine-learned model from adefined model search space, wherein the defined model search spacecomprises one or more searchable parameters, wherein the firstmachine-learned model comprises a one or more first values for the oneor more searchable parameters. The operations can include performing amodel training process on the first machine-learned model to obtainfirst training data descriptive of a first training speed. Theoperations can include generating a second machine-learned model fromthe defined model search space based at least in part on the firsttraining data, wherein the second machine-learned model comprises one ormore second values for the one or more searchable parameters, wherein atleast one of the one or more second values is different than the one ormore first values, wherein the second machine-learned model comprises aplurality of sequential model stages, wherein each model stage comprisesone or more model layers, and wherein a first model stage comprisesfewer model layers than a second model stage of the plurality of modelstages. The operations can include performing the model training processon the second machine-learned model to obtain second training datadescriptive of a second training speed, wherein the second trainingspeed is faster than the first training speed.

Another example aspect is directed to one or more tangible,non-transitory computer readable media that store: a machine-learnedmodel, comprising: a first sequence of a plurality of Fused-MBConvstages; and a second sequence of a plurality of MBConv stages, whereinthe second sequence of the plurality of MBConv stages follows the firstsequence of the plurality of Fused-MBConv stages; and computer-readableinstructions that when executed by the one or more processors cause theone or more processors to perform operations, the operations comprising:obtaining a model input; and processing the model input with themachine-learned model to generate a model output. In someimplementations, the plurality of Fused-MBConv stages consists of threeFused-MBConv stages. In some implementations, the three Fused-MBConvstages comprises a first, a second, and a third Fused-MBConv stage thathave 2, 4, and 4 layers, respectively. In some implementations, thethree Fused-MBConv stages comprises a first, a second, and a thirdFused-MBConv stage that have 24, 48, and 64 channels, respectively. Insome implementations, the three Fused-MBConv stages comprises a first, asecond, and a third Fused-MBConv stage that each have 3×3 kernels.

Other aspects of the present disclosure are directed to various systems,apparatuses, non-transitory computer-readable media, user interfaces,and electronic devices.

These and other features, aspects, and advantages of various embodimentsof the present disclosure will become better understood with referenceto the following description and appended claims. The accompanyingdrawings, which are incorporated in and constitute a part of thisspecification, illustrate example embodiments of the present disclosureand, together with the description, serve to explain the relatedprinciples.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed discussion of embodiments directed to one of ordinary skill inthe art is set forth in the specification, which makes reference to theappended figures, in which:

FIG. 1A depicts a block diagram of an example computing system thatperforms model training using progressive regularization according toexample embodiments of the present disclosure.

FIG. 1B depicts a block diagram of an example computing device thatperforms model training using progressive regularization according toexample embodiments of the present disclosure.

FIG. 1C depicts a block diagram of an example computing device thatperforms generation of machine-learned models with optimized trainingspeed according to example embodiments of the present disclosure.

FIG. 2A shows Fused-MBConv and MBConv architectures according to exampleembodiments of the present disclosure.

FIG. 2B depicts a block diagram of an example machine-learned modelgenerated through an architecture search technique to emphasize trainingspeed according to example embodiments of the present disclosure.

FIG. 3 depicts a graphical diagram of an example neural architecturesearch approach to emphasize training speed and accuracy according toexample embodiments of the present disclosure.

FIG. 4 depicts a data flow diagram of an example method for generationof machine-learned models with optimized training speed.

FIG. 5 depicts a flow chart diagram of an example method to performmodel training using progressive regularization according to exampleembodiments of the present disclosure

Reference numerals that are repeated across plural figures are intendedto identify the same features in various implementations.

DETAILED DESCRIPTION Overview

Generally, the present disclosure is directed to progressive learning ofmachine-learned models. More particularly, the present disclosurerelates to progressive adjustment of regularization during training of amachine-learned model to optimize training speed. As an example, aplurality of training samples (e.g., training images, training datasets,etc.) can be obtained for a machine-learned model (e.g., a convolutionalneural network, a deep learning network, etc.). For one or more trainingiterations, the machine-learned model can be trained using one or moreof these training samples based on a first regularization magnitude. Thefirst regularization magnitude can be configured to control a relativeeffect of one or more regularization techniques (e.g., model dropout(s),training data augmentation, etc.). For one or more second trainingiterations, the model can be trained based at least in part on a secondregularization magnitude that is greater than the first regularizationmagnitude. Additionally, in some implementations, the complexity of thetraining samples (e.g., an image size, a dataset size, etc.) can beprogressively increased in a substantially similar manner. By initiallytraining the model with a relatively weak level of regularization anddata complexity, and then increasing both parameters progressively,systems and methods of the present disclosure substantially reduce thecomputational resources required during earlier training iterations,therefore increasing the accuracy of the model while simultaneouslyincreasing the overall speed of training the model.

To further increase the training efficiency of machine-learned models,the architecture of the models can be selected based an architecturesearch (e.g., neural architecture search, etc.) that emphasizes trainingefficiency. As an example, a first machine-learned model can begenerated from a defined model search space that includes one or moreparameters. More particularly, the first machine-learned model caninclude one or more values for the one or more parameters. A modeltraining process (e.g., the previously described progressiveregularization, etc.) can be applied to the first machine-learned modelto obtain training data descriptive of a first training speed. Based atleast in part on the first training data, a second machine-learned modelcan be generated from the defined model search space that includes oneor more second values different than the one or more first values. Themodel training process can be performed on the second machine-learnedmodel to obtain second training data. The second training data candescribe a second training speed faster than the first training speed.In such fashion, an architecture search technique can be utilized tooptimize for training speed, therefore significantly increasing theoverall training speed of the machine-learned model(s).

In some implementations, the complexity of the training data can beprogressively increased alongside the regularization magnitude duringtraining. As an example, a first complexity (e.g., an image size, etc.)can be determined for a first set of training images (e.g., 280×280pixels, etc.). The first set of training images can be used in atraining process to train a machine-learned model at a firstregularization magnitude. A second complexity can be determined for asecond set of training images (e.g., 1080×720 pixels, etc.) that isgreater than the first complexity, alongside a second regularizationmagnitude greater than the first. The second set of training images canbe used in the training process to train the machine-learned model basedon the second regularization magnitude. In such fashion, both theaccuracy and training efficiency (e.g., speed, number of trainingepochs, etc.) can be increased substantially.

In some implementations, training complexity can be, represent, orotherwise describe a difficulty associated with processing a trainingsample. As an example, the training sample can be image data. Increasingthe training complexity of the image data can include augmenting one ormore characteristics of the image data (e.g., increasing resolution,increasing color of the image data, increasing a number of imagefeatures, adding noise to the image, rotating the image, augmenting theimage with a second image, etc.). As another example, if the trainingsample is a polygonal mesh, increasing the training complexity of thepolygonal mesh may include increasing a number of included polygons. Assuch, it should be broadly understood that adjustment of the trainingcomplexity of a training sample can be accomplished in any way thatadjusts the difficulty associated with processing of the training sampleby the respective machine-learned model.

In some implementations, the model(s) of the present disclosure can beor otherwise include attention-based models. As an example, the model(s)of the present disclosure may include one or more self-attentionmechanisms, and/or one or more attention-level layers. For example, themodel(s) of the present disclosure may be or otherwise includetransformer model(s). In some implementations, regularizationtechnique(s) may include augmentation of the attention scope in thesemodels. For example, a regularization technique may augment a fullself-attention layer in some fashion (e.g., adjustment of attentionalweight(s), modification of the attention architecture, modifying scopein which attention is determined between attention heads of the layer,etc.).

The systems and methods described herein provide a number of technicaleffects and benefits. As one example, by progressively increasing themagnitude of regularization during training, the systems and methods ofthe present disclosure can substantially reduce the overall complexityand computational resources required to train a model (e.g., lessprocessing power, less memory usage, less power consumption, etc.), ascompared to, for example, conventional training techniques which utilizea continuous magnitude of regularization. Thus, the proposed techniquescan enable more efficient training of machine learning models which usesfewer computational resources.

Furthermore, by generating a model with an architecture optimized fortraining speed and efficiency, systems and methods of the presentdisclosure can further reduce the overall time and computationalresources required for model training. For example, by leveraging anarchitecture search technique to generate a segmented machine-learnedmodel with increasing complexity (e.g., number of layers, etc.) persegment, the systems and methods of the present disclosure can furtherenhance the efficacy of progressive regularization during training,therefore reducing computational resource requirements even further.

As another example technical effect and benefit, progressive adjustmentof training complexity (e.g., a size of training data, a relative“difficulty” of correct processing of training data, etc.) is generallyknown to increase training speed. However, it can also lead to reducedmodel accuracy. By progressively increasing the regularization oftraining in a corresponding manner, increases in training speed fromprogressive training complexity can be enhanced, while also amelioratingany reductions in model accuracy. As such, the progressiveregularization of the model during training can enhance other methodsfor increasing training speed while also substantially reducing oreliminating any reduction in accuracy from those methods.

As another example technical effect and benefit, meta-learning (e.g.,neural architecture search, etc.) techniques often utilize “early stop”techniques when generating machine-learned model(s). For example, ameta-learning technique may implement an early halt to cycle of trainingiterations based on the initial results of the training iterations. Byutilizing a progressive regularization technique, earlier trainingiterations in a training cycle are less computationally expensive. Byreducing the overall computational cost associated with early trainingiterations, systems and methods of the present disclosure cansubstantially reduce the negative effects associated with early stops inmeta-learning techniques (e.g., wasted compute resources, etc.).

Thus, example aspects of the present disclosure are directed to a newfamily of convolutional networks that have faster training speed andbetter parameter efficiency than previous models. To develop thesemodels, example systems described herein can use a combination oftraining-aware neural architecture search and scaling, to jointlyoptimize training speed and parameter efficiency. The models weresearched from the search space enriched with new ops such asFused-MBConv. Experiments show that the models proposed herein trainmuch faster than state-of-the-art models while being up to 6.8× smaller.

Furthermore, training of models can be further sped up by progressivelyincreasing the image size during training. However, this often causes adrop in accuracy. To compensate for this accuracy drop, the presentdisclosure proposes an improved method of progressive learning, whichadaptively adjusts regularization (e.g. data augmentation) e.g., inaddition to image size.

More particularly, training efficiency has gained significant interestsrecently. For instance, NFNets aim to improve training efficiency byremoving the expensive batch normalization; Several recent works focuson improving training speed by adding attention layers intoconvolutional networks (ConvNets); Vision Transformers improves trainingefficiency on large-scale datasets by using Transformer blocks.

However, these methods often come with expensive overhead on largeparameter size. In contrast, the present disclosure uses an combinationof training-aware neural architecture search (NAS) and scaling toimprove both training speed and parameter efficiency. Given theparameter efficiency of certain models known as EfficientNets (see Tan,M. and Le, Q. V. EfficientNet: Rethinking model scaling forconvolutional neural networks. ICML, 2019a) the present disclosuresystematically studying the training bottlenecks in EfficientNets. Thesestudies shows in EfficientNets: (1) training with very large image sizesis slow; (2) depthwise convolutions are slow in early layers. (3)equally scaling up every stage is sub-optimal.

Based on these observations, the present disclosure provides a searchspace enriched with additional operations such as Fused-MBConv. Thepresent disclosure also applies training-aware NAS and scaling tojointly optimize model accuracy, training speed, and parameter size. Theresulting networks, which can be referred to as EfficientNetV2, train upto 4× faster than prior models, while being up to 6.8× smaller inparameter size.

In some example implementations, training can be further sped up byprogressively increasing image size during training. Many previous workshave used smaller image sizes in training; however, they keep the sameregularization for all image sizes, causing a drop in accuracy. Thus,keeping the same regularization for different image sizes is not ideal:for the same network, small image size leads to small network capacityand thus requires weak regularization; vice versa, large image sizerequires stronger regularization to combat overfitting.

Based on this insight, the present disclosure proposes an improvedmethod of progressive learning: in the early training epochs, thenetwork can be trained with small image size and weak regularization(e.g., dropout and data augmentation), then image size can gradually beincreased and stronger regularization can be added. This approach canspeed up the training without causing accuracy drop.

With the improved progressive learning, example implementations of theproposed EfficientNetV2 achieves strong results on ImageNet, CIFAR-10,CIFAR-100, Cars, and Flowers dataset. On ImageNet, EfficientNet V2achieves 85.7% top-1 accuracy while training 3×-9× faster and being upto 6.8× smaller than previous models. The EfficientNetV2 and progressivelearning also make it easier to train models on larger datasets. Forexample, ImageNet21k is about 10× larger than ImageNet ILSVRC2012, butthe EfficientNetV2 can finish the training within two days usingmoderate computing resources of 32 TPUv3 cores. By pretraining on thepublic ImageNet21k, the EfficientNetV2 achieves 87.3% top-1 accuracy onImageNet ILSVRC2012, outperforming the recent ViT-L/16 by 2.0% accuracywhile training 5×-11× faster.

Thus, the present disclosure provides the following advances:EfficientNetV2, a new family of smaller and faster models. Found by ourtraining-aware NAS and scaling, EfficientNetV2 outperform previousmodels in both training speed and parameter efficiency. An improvedmethod of progressive learning, which adaptively adjusts regularizationalong with image size. The method speeds up training, and simultaneouslyimproves accuracy. The proposed approaches demonstrate up to 11× fastertraining speed and up to 6.8× better parameter efficiency on ImageNet,CIFAR, Cars, and Flowers dataset, than prior art.

Example EfficientNetV2 Architecture Design

This section studies the training bottlenecks of EfficientNet andintroduces the proposed training-aware NAS and scaling, as well asEfficientNetV2 models.

Review of EfficientNet

EfficientNet is a family of models that are optimized for FLOPs andparameter efficiency. It leverages NAS to search for the baselineEfficientNet-B0 that has better trade-off on accuracy and FLOPs. Thebaseline model is then scaled up with a compound scaling strategy toobtain a family of models B1-B7. While recent works have claimed largegains on training or inference speed, they are often worse thanEfficientNet in terms of parameters and FLOPs efficiency. The presentdisclosure improves the training speed while maintaining the parameterefficiency.

Understanding Training Efficiency

The section describes the training bottlenecks of EfficientNet(henceforth called EfficientNetV1) and a few simple techniques toimprove training speed. Training with very large image sizes is slow: Aspointed out by previous works, EfficientNetv1's large image size resultsin significant memory usage. Since the total memory on GPU/TPU is fixed,these models are generally trained with smaller batch size, whichdrastically slows down the training. A simple improvement is to applyFixRes, by using a smaller image size for training than for inference.Smaller image size leads to less computations and enables large batchsize, and thus improves training speed by up to 2.2×. Using smallerimage size for training also leads to slightly better accuracy. In someimplementations, no layers are finetuned after training. Depthwiseconvolutions are slow in early layers but effective in later stages:Another training bottleneck of EfficientNetv1 comes from the extensivedepthwise convolutions. Depthwise convolutions have fewer parameters andFLOPs than regular convolutions, but they often cannot fully utilizemodern accelerators. Recently, Fused-MBConv has been used to betterutilize mobile or server accelerators. Fused-MBConv is described inGupta, S. and Tan, M. EfficientNet-EdgeTPU: Creatingaccelerator-optimized neural networks with automl. https:ai.googleblog.com/2019/08/efficientnetedgetpu-creating.html, 2019.Fused-MBConv replaces the depthwise conv3×3 and expansion conv1×1 inMBConv with a single regular conv3×3. MBConv is described in Sandler etal., Mobilenetv2: Inverted residuals and linear bottlenecks. CVPR, 2018and Tan, M. and Le, Q. V. EfficientNet: Rethinking model scaling forconvolutional neural networks. ICML, 2019a. The Fused-MBConv and MBConvarchitectures are shown in FIG. 2A.

To systematically compare these two building blocks, example experimentsgradually replaced the original MBConv in EfficientNet-B4 withFused-MBConv. When applied in early stage 1-3, Fused-MBConv can improvetraining speed with a small overhead on parameters and FLOPs, but if wereplace all blocks with Fused-MBConv (stage 1-7), then it significantlyincreases parameters and FLOPs while also slowing down the training.Finding the right combination of these two building blocks, MBConv andFused-MBConv, is non-trivial, which is a problem solved by the presentdisclosure through the use of neural architecture search toautomatically search for the best combination. Equally scaling up everystage is sub-optimal: EfficientNetv1 equally scales up all stages usinga simple compound scaling rule. For example, when depth coefficient is2, then all stages in the networks would double the number of layers.However, these stages are not equally contributed to the training speedand parameter efficiency. Example implementations of the presentdisclosure use a non-uniform scaling strategy to gradually add morelayers to later stages. In addition, v1 EfficientNets aggressively scaleup image size, leading to large memory consumption and slow training. Toaddress this issue, example implementations of the present disclosureslightly modify the scaling rule and restrict the maximum image size toa smaller value. Example Training-Aware NAS and Scaling

Example implementations of the present disclosure provide multipledesign choices for improving training speed. To search for the bestcombinations of those choices, this section proposes a training-awareNAS. NASSearch: An example training-aware NAS framework proposed by thepresent disclosure aims to jointly optimize accuracy, parameterefficiency, and training efficiency on modern accelerators.Specifically, the NAS uses EfficientNet as its backbone. The searchspace can be a stage-based factorized space similar to Tan et al.,Mnasnet: Platform-aware neural architecture search for mobile. CVPR,2019, but which consists of the design choices for convolutionaloperation types {MBConv, Fused-MBConv}, number of layers, kernel size{3×3, 5×5}, expansion ratio {1, 4, 6}. On the other hand, the searchspace size can optionally be reduced by (1) removing unnecessary searchoptions such as pooling skip ops, since they are never used in theoriginal EfficientNets; (2) reusing the same channel sizes from thebackbone as they are already searched in the original EfficientNets.Since the search space is smaller, the search process can applyreinforcement learning or simply random search on much larger networksthat have comparable size as EfficientNet-B4. Specifically, an examplesearch approach can sample up to 1000 models and train each model about10 epochs with reduced image size for training. An example search rewardcan combine the model accuracy A, the normalized training step time S,and the parameter size P, using a simple weighted product A·S^(w)·P^(v),where w=−0.07 and v=−0.05 are empirically determined to balance thetrade-offs.

The table below shows one example architecture for the searched modelEfficientNetV2−S. Compared to the EfficientNet backbone, the proposedsearched EfficientNetV2 has several major distinctions: (1) The firstdifference is EfficientNetV2 extensively uses both MBConv and the newlyadded fused-BConv in the early layers. (2) Secondly, EfficientNetV2prefers smaller expansion ratio for MBConv since smaller expansionratios tend to have less memory access overhead. (3) Thirdly,EfficientNetV2 prefers smaller 3×3 kernel sizes, but it adds more layersto compensate the reduced receptive field resulted from the smallerkernel size. (4) Lastly, EfficientNetV2 completely removes the laststride-1 stage in the original EfficientNet, perhaps due to its largeparameter size and memory access overhead.

EfficientNetV2-S architecture: Stage Operator Stride #Channels #Layers 0Conv3 × 3 2   24  1 1 Fused-MBConv1, 1   24  2 k3 × 3 2 Fused-MBConv4, 2  48  4 k3 × 3 3 Fused-MBConv4, 2   64  4 k3 × 3 4 MBConv4, k3 × 3, 2 128  6 SE0.25 5 MBConv6, k3 × 3, 1  160  9 SE0.25 6 MBConv6, k3 × 3, 2 256 15 SE0.25 7 Conv1 × 1 & — 1280  1 Pooling & FC

Another example architecture is shown in the table below:

ETNet-S architecture: Stage Operator Stride #Channels #Layers 1 Conv3 ×3 2 32 1 2 Fused-MBConv1, 1 24 2 k3 × 3 3 Fused-MBConv4, 2 48 4 k3 × 3 4Fused-MBConv4, 2 64 4 k3 × 3 5 MBConv4, k3 × 3, 2 128 6 SE 6 MBConv6, k5× 5, 1 160 6 SE 7 MBConv6, k5 × 5, 2 272 8 SE 8 MBConv6, k3 × 3, 1 448 2SE 9 Conv1 × 1 & — 1792 1 Pooling & FC

EfficientNetV2 Scaling: Some example implementations can scale upEfficientNetV2−S to obtain EfficientNetV2−M/L using similar compoundscaling as described in EfficientNetv1, with a few additionaloptimizations: (1) the maximum inference image size can be restricted to480, as very large images often lead to expensive memory and trainingspeed overhead; (2) as a heuristic, more layers can gradually be addedto later stages (e.g., stage 5 and 6) in order to increase the networkcapacity without adding much runtime overhead.

Example Progressive Learning Approaches Example Motivation

As discussed in the previous section, image size plays an important rolein training efficiency. In addition to FixRes, many other worksdynamically change image sizes during training, but they often cause adrop in accuracy. This accuracy drop likely comes from the unbalancedregularization: when training with different image sizes, it is alsobest to also adjust the regularization strength accordingly (instead ofusing a fixed regularization as in previous works). In fact, it iscommon that large models require stronger regularization to combatoverfitting: for example, EfficientNet-B7 uses larger dropout andstronger data augmentation than the B0. The present disclosure proposesthat even for the same network, smaller image size leads to smallernetwork capacity and thus needs weaker regularization; vice versa,larger image size leads to more computations with larger capacity, andthus more vulnerable to overfitting.

Example Progressive Learning with Adaptive Regularization

One example training process with improved progressive learning is asfollows: in the early training epochs, the network is trained withsmaller images and weak regularization, such that the network can learnsimple representations easily and fast. Then, image size can begradually increased but learning is also made more difficult by addingstronger regularization.

Formally, suppose the whole training has N total steps, the target imagesize is S_(e), with a list of regularization magnitude Φ_(e)={ϕ_(e)^(k)}, where k represents a type of regularization such as dropout rateor mixup rate value. Some example implementations divide the traininginto M stages: for each stage 1≤i≤M, the model can be trained with imagesize S_(i) and regularization magnitude ϕ_(i)={=ϕ_(i) ^(k)}. The laststage M would use the targeted image size S_(e) and regularizationΦ_(e). For simplicity, some example implementations heuristically pickthe initial image size S₀ and regularization Φ₀, and then use a linearinterpolation to determine the value for each stage. One exampleAlgorithm is provided below. At the beginning of each stage, the networkwill inherit all weights from the previous stage. Unlike transformers,whose weights (e.g., position embedding) may depend on input length,ConvNet weights are independent to image sizes and thus can be inheritedeasily.

Example Algorithm for Progressive Learning with Adaptive Regularization:

Input: Initial image size S₀ and regularization {ϕ₀ ^(k)}.

Input: Final image size S_(e) and regularization {ϕ_(e) ^(k)}.

Input: Number of total training steps N and stages M.

for i=0 to M−1 do

Image size:

$\left. S_{i}\leftarrow{S_{0} + {\left( {S_{e} - S_{0}} \right) \cdot \frac{i}{M - 1}}} \right.$

Regularization:

$\left. R_{i}\leftarrow\left\{ {\phi_{i}^{k} = {\phi_{0}^{k} + {\left( {\phi_{e}^{k} - \phi_{0}^{k}} \right) \cdot \frac{i}{M - 1}}}} \right\} \right.$

Train the model N/M for steps with S_(i) and R_(i).

end for

Example implementations of the proposed progressive learning aregenerally compatible to any existing regularization. As examples, thefollowing types of regularization can be progressively adapted asdescribed herein:

Dropout: a network-level regularization, which reduces co-adaptation byrandomly dropping channels. Progressive learning can be applied toadjust the dropout rate γ. Dropout is described in Srivastava et al.Dropout: a simple way to prevent neural networks from overfitting. TheJournal of Machine Learning Research, 15(1):1929-1958, 2014.

RandAugment: a per-image data augmentation, with adjustable magnitude ε.Progressive learning can be applied to adjust the magnitude. RandAugmentis described at Cubuk et al., Randaugment: Practical automated dataaugmentation with a reduced search space. ECCV, 2020.

Mixup: a cross-image data augmentation. Given two images with labels(x_(i), y_(i)) and (x_(j), y_(j)), it combines them with mixup ratio λ:{tilde over (x)}_(i)=λx_(j)+(1−λ)x_(i) and {tilde over(y)}_(i)=λy_(j)+(1−λ)y_(i). Progressive learning can be applied toadjust mixup ratio λ during training. Mixup is described at Zhang etal., Mixup: Beyond empirical risk minimization. ICLR, 2018.

Example Devices and Systems

FIG. 1A depicts a block diagram of an example computing system 100 thatperforms model training using progressive regularization according toexample embodiments of the present disclosure. The system 100 includes auser computing device 102, a server computing system 130, and a trainingcomputing system 150 that are communicatively coupled over a network180.

The user computing device 102 can be any type of computing device, suchas, for example, a personal computing device (e.g., laptop or desktop),a mobile computing device (e.g., smartphone or tablet), a gaming consoleor controller, a wearable computing device, an embedded computingdevice, or any other type of computing device.

The user computing device 102 includes one or more processors 112 and amemory 114. The one or more processors 112 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, anFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 114can include one or more non-transitory computer-readable storage media,such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks,etc., and combinations thereof. The memory 114 can store data 116 andinstructions 118 which are executed by the processor 112 to cause theuser computing device 102 to perform operations.

In some implementations, the user computing device 102 can store orinclude one or more machine-learned models 120. For example, themachine-learned models 120 can be or can otherwise include variousmachine-learned models such as neural networks (e.g., deep neuralnetworks) or other types of machine-learned models, including non-linearmodels and/or linear models. Neural networks can include feed-forwardneural networks, recurrent neural networks (e.g., long short-term memoryrecurrent neural networks), convolutional neural networks or other formsof neural networks. Some example machine-learned models can leverage anattention mechanism such as self-attention. For example, some examplemachine-learned models can include multi-headed self-attention models(e.g., transformer models).

In some implementations, the one or more machine-learned models 120 canbe received from the server computing system 130 over network 180,stored in the user computing device memory 114, and then used orotherwise implemented by the one or more processors 112. In someimplementations, the user computing device 102 can implement multipleparallel instances of a single machine-learned model 120 (e.g., forparallel progressive regularization training across multiple instancesof the model).

Additionally, or alternatively, one or more machine-learned models 140can be included in or otherwise stored and implemented by the servercomputing system 130 that communicates with the user computing device102 according to a client-server relationship. For example, themachine-learned models 140 can be implemented by the server computingsystem 140 as a portion of a web service (e.g., a classificationservice, a prediction service, etc.). Thus, one or more models 120 canbe stored and implemented at the user computing device 102 and/or one ormore models 140 can be stored and implemented at the server computingsystem 130.

The user computing device 102 can also include one or more user inputcomponents 122 that receives user input. For example, the user inputcomponent 122 can be a touch-sensitive component (e.g., atouch-sensitive display screen or a touch pad) that is sensitive to thetouch of a user input object (e.g., a finger or a stylus). Thetouch-sensitive component can serve to implement a virtual keyboard.Other example user input components include a microphone, a traditionalkeyboard, or other means by which a user can provide user input.

The server computing system 130 includes one or more processors 132 anda memory 134. The one or more processors 132 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, anFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 134can include one or more non-transitory computer-readable storage media,such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks,etc., and combinations thereof. The memory 134 can store data 136 andinstructions 138 which are executed by the processor 132 to cause theserver computing system 130 to perform operations.

In some implementations, the server computing system 130 includes or isotherwise implemented by one or more server computing devices. Ininstances in which the server computing system 130 includes pluralserver computing devices, such server computing devices can operateaccording to sequential computing architectures, parallel computingarchitectures, or some combination thereof.

As described above, the server computing system 130 can store orotherwise include one or more machine-learned models 140. For example,the models 140 can be or can otherwise include various machine-learnedmodels. Example machine-learned models include neural networks or othermulti-layer non-linear models. Example neural networks include feedforward neural networks, deep neural networks, recurrent neuralnetworks, and convolutional neural networks. Some examplemachine-learned models can leverage an attention mechanism such asself-attention. For example, some example machine-learned models caninclude multi-headed self-attention models (e.g., transformer models).

The user computing device 102 and/or the server computing system 130 cantrain the models 120 and/or 140 via interaction with the trainingcomputing system 150 that is communicatively coupled over the network180. The training computing system 150 can be separate from the servercomputing system 130 or can be a portion of the server computing system130.

The training computing system 150 includes one or more processors 152and a memory 154. The one or more processors 152 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, anFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 154can include one or more non-transitory computer-readable storage media,such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks,etc., and combinations thereof. The memory 154 can store data 156 andinstructions 158 which are executed by the processor 152 to cause thetraining computing system 150 to perform operations. In someimplementations, the training computing system 150 includes or isotherwise implemented by one or more server computing devices.

The training computing system 150 can include a model trainer 160 thattrains the machine-learned models 120 and/or 140 stored at the usercomputing device 102 and/or the server computing system 130 usingvarious training or learning techniques, such as, for example, backwardspropagation of errors. For example, a loss function can bebackpropagated through the model(s) to update one or more parameters ofthe model(s) (e.g., based on a gradient of the loss function). Variousloss functions can be used such as mean squared error, likelihood loss,cross entropy loss, hinge loss, and/or various other loss functions.Gradient descent techniques can be used to iteratively update theparameters over a number of training iterations.

In some implementations, performing backwards propagation of errors caninclude performing truncated backpropagation through time. The modeltrainer 160 can perform a number of generalization techniques (e.g.,weight decays, dropouts, etc.) to improve the generalization capabilityof the models being trained.

In particular, the model trainer 160 can train the machine-learnedmodels 120 and/or 140 based on a set of training data 162. As anexample, the training data 162 can include a plurality of trainingsamples (e.g., training images, training datasets, etc.) for amachine-learned model (e.g., model(s) 120, model(s) 140, etc.). For oneor more training iterations, the machine-learned model(s) 120/140 can betrained using one or more of these training samples 162 based on a firstregularization magnitude (e.g., using the model trainer 160). The firstregularization magnitude can be configured for the model trainer 160 tocontrol a relative effect of one or more regularization techniques(e.g., model dropout(s), training data augmentation, etc.). For one ormore second training iterations, the model(s) 120/140 can be trained bythe model trainer 160 based at least in part on a second regularizationmagnitude that is greater than the first regularization magnitude.Additionally, in some implementations, the complexity of the trainingsamples 162 (e.g., an image size, a dataset size, etc.) can beprogressively increased in a substantially similar manner (e.g., usingthe model trainer 160). By increasing the regularization magnitudeprogressively over a number of training iterations, the accuracy of themodel(s) 120/140 can be increased while simultaneously increasing theoverall training efficiency of the model(s) 120/140.

In some implementations, if a user has provided consent, the trainingexamples can be provided by the user computing device 102. Thus, in suchimplementations, the model 120 provided to the user computing device 102can be trained by the training computing system 150 on user-specificdata received from the user computing device 102. In some instances,this process can be referred to as personalizing the model.

The model trainer 160 includes computer logic utilized to providedesired functionality. The model trainer 160 can be implemented inhardware, firmware, and/or software controlling a general purposeprocessor. For example, in some implementations, the model trainer 160includes program files stored on a storage device, loaded into a memoryand executed by one or more processors. In other implementations, themodel trainer 160 includes one or more sets of computer-executableinstructions that are stored in a tangible computer-readable storagemedium such as RAM, hard disk, or optical or magnetic media.

The network 180 can be any type of communications network, such as alocal area network (e.g., intranet), wide area network (e.g., Internet),or some combination thereof and can include any number of wired orwireless links. In general, communication over the network 180 can becarried via any type of wired and/or wireless connection, using a widevariety of communication protocols (e.g., TCP/IP, HTTP, SMTP, FTP),encodings or formats (e.g., HTML, XML), and/or protection schemes (e.g.,VPN, secure HTTP, SSL).

In some implementations, the input to the machine-learned model(s) ofthe present disclosure can be image data. The machine-learned model(s)can process the image data to generate an output. As an example, themachine-learned model(s) can process the image data to generate an imagerecognition output (e.g., a recognition of the image data, a latentembedding of the image data, an encoded representation of the imagedata, a hash of the image data, etc.). As another example, themachine-learned model(s) can process the image data to generate an imagesegmentation output. As another example, the machine-learned model(s)can process the image data to generate an image classification output.As another example, the machine-learned model(s) can process the imagedata to generate an image data modification output (e.g., an alterationof the image data, etc.). As another example, the machine-learnedmodel(s) can process the image data to generate an encoded image dataoutput (e.g., an encoded and/or compressed representation of the imagedata, etc.). As another example, the machine-learned model(s) canprocess the image data to generate an upscaled image data output. Asanother example, the machine-learned model(s) can process the image datato generate a prediction output.

In some implementations, the input to the machine-learned model(s) ofthe present disclosure can be text or natural language data. Themachine-learned model(s) can process the text or natural language datato generate an output. As an example, the machine-learned model(s) canprocess the natural language data to generate a language encodingoutput. As another example, the machine-learned model(s) can process thetext or natural language data to generate a latent text embeddingoutput. As another example, the machine-learned model(s) can process thetext or natural language data to generate a translation output. Asanother example, the machine-learned model(s) can process the text ornatural language data to generate a classification output. As anotherexample, the machine-learned model(s) can process the text or naturallanguage data to generate a textual segmentation output. As anotherexample, the machine-learned model(s) can process the text or naturallanguage data to generate a semantic intent output. As another example,the machine-learned model(s) can process the text or natural languagedata to generate an upscaled text or natural language output (e.g., textor natural language data that is higher quality than the input text ornatural language, etc.). As another example, the machine-learnedmodel(s) can process the text or natural language data to generate aprediction output.

In some implementations, the input to the machine-learned model(s) ofthe present disclosure can be speech data. The machine-learned model(s)can process the speech data to generate an output. As an example, themachine-learned model(s) can process the speech data to generate aspeech recognition output. As another example, the machine-learnedmodel(s) can process the speech data to generate a speech translationoutput. As another example, the machine-learned model(s) can process thespeech data to generate a latent embedding output. As another example,the machine-learned model(s) can process the speech data to generate anencoded speech output (e.g., an encoded and/or compressed representationof the speech data, etc.). As another example, the machine-learnedmodel(s) can process the speech data to generate an upscaled speechoutput (e.g., speech data that is higher quality than the input speechdata, etc.). As another example, the machine-learned model(s) canprocess the speech data to generate a textual representation output(e.g., a textual representation of the input speech data, etc.). Asanother example, the machine-learned model(s) can process the speechdata to generate a prediction output.

In some implementations, the input to the machine-learned model(s) ofthe present disclosure can be latent encoding data (e.g., a latent spacerepresentation of an input, etc.). The machine-learned model(s) canprocess the latent encoding data to generate an output. As an example,the machine-learned model(s) can process the latent encoding data togenerate a recognition output. As another example, the machine-learnedmodel(s) can process the latent encoding data to generate areconstruction output. As another example, the machine-learned model(s)can process the latent encoding data to generate a search output. Asanother example, the machine-learned model(s) can process the latentencoding data to generate a reclustering output. As another example, themachine-learned model(s) can process the latent encoding data togenerate a prediction output.

In some implementations, the input to the machine-learned model(s) ofthe present disclosure can be statistical data. Statistical data can be,represent, or otherwise include data computed and/or calculated fromsome other data source. The machine-learned model(s) can process thestatistical data to generate an output. As an example, themachine-learned model(s) can process the statistical data to generate arecognition output. As another example, the machine-learned model(s) canprocess the statistical data to generate a prediction output. As anotherexample, the machine-learned model(s) can process the statistical datato generate a classification output. As another example, themachine-learned model(s) can process the statistical data to generate asegmentation output. As another example, the machine-learned model(s)can process the statistical data to generate a visualization output. Asanother example, the machine-learned model(s) can process thestatistical data to generate a diagnostic output.

In some implementations, the input to the machine-learned model(s) ofthe present disclosure can be sensor data. The machine-learned model(s)can process the sensor data to generate an output. As an example, themachine-learned model(s) can process the sensor data to generate arecognition output. As another example, the machine-learned model(s) canprocess the sensor data to generate a prediction output. As anotherexample, the machine-learned model(s) can process the sensor data togenerate a classification output. As another example, themachine-learned model(s) can process the sensor data to generate asegmentation output. As another example, the machine-learned model(s)can process the sensor data to generate a visualization output. Asanother example, the machine-learned model(s) can process the sensordata to generate a diagnostic output. As another example, themachine-learned model(s) can process the sensor data to generate adetection output.

In some cases, the machine-learned model(s) can be configured to performa task that includes encoding input data for reliable and/or efficienttransmission or storage (and/or corresponding decoding). For example,the task may be audio compression task. The input may include audio dataand the output may comprise compressed audio data. In another example,the input includes visual data (e.g. one or more image or videos), theoutput comprises compressed visual data, and the task is a visual datacompression task. In another example, the task may comprise generatingan embedding for input data (e.g. input audio or visual data).

In some cases, the input includes visual data and the task is a computervision task. In some cases, the input includes pixel data for one ormore images and the task is an image processing task. For example, theimage processing task can be image classification, where the output is aset of scores, each score corresponding to a different object class andrepresenting the likelihood that the one or more images depict an objectbelonging to the object class. The image processing task may be objectdetection, where the image processing output identifies one or moreregions in the one or more images and, for each region, a likelihoodthat region depicts an object of interest. As another example, the imageprocessing task can be image segmentation, where the image processingoutput defines, for each pixel in the one or more images, a respectivelikelihood for each category in a predetermined set of categories. Forexample, the set of categories can be foreground and background. Asanother example, the set of categories can be object classes. As anotherexample, the image processing task can be depth estimation, where theimage processing output defines, for each pixel in the one or moreimages, a respective depth value. As another example, the imageprocessing task can be motion estimation, where the network inputincludes multiple images, and the image processing output defines, foreach pixel of one of the input images, a motion of the scene depicted atthe pixel between the images in the network input.

In some cases, the input includes audio data representing a spokenutterance and the task is a speech recognition task. The output maycomprise a text output which is mapped to the spoken utterance. In somecases, the task comprises encrypting or decrypting input data. In somecases, the task comprises a microprocessor performance task, such asbranch prediction or memory address translation.

FIG. 1A illustrates one example computing system that can be used toimplement the present disclosure. Other computing systems can be used aswell. For example, in some implementations, the user computing device102 can include the model trainer 160 and the training dataset 162. Insuch implementations, the models 120 can be both trained and usedlocally at the user computing device 102. In some of suchimplementations, the user computing device 102 can implement the modeltrainer 160 to personalize the models 120 based on user-specific data.

FIG. 1B depicts a block diagram of an example computing device 10 thatperforms model training using progressive regularization according toexample embodiments of the present disclosure. The computing device 10can be a user computing device or a server computing device.

The computing device 10 includes a number of applications (e.g.,applications 1 through N). Each application contains its own machinelearning library and machine-learned model(s). For example, eachapplication can include a machine-learned model. Example applicationsinclude a text messaging application, an email application, a dictationapplication, a virtual keyboard application, a browser application, etc.

As illustrated in FIG. 1B, each application can communicate with anumber of other components of the computing device, such as, forexample, one or more sensors, a context manager, a device statecomponent, and/or additional components. In some implementations, eachapplication can communicate with each device component using an API(e.g., a public API). In some implementations, the API used by eachapplication is specific to that application.

FIG. 1C depicts a block diagram of an example computing device 50 thatperforms generation of machine-learned models with optimized trainingspeed according to example embodiments of the present disclosure. Thecomputing device 50 can be a user computing device or a server computingdevice.

The computing device 50 includes a number of applications (e.g.,applications 1 through N). Each application is in communication with acentral intelligence layer. Example applications include a textmessaging application, an email application, a dictation application, avirtual keyboard application, a browser application, etc. In someimplementations, each application can communicate with the centralintelligence layer (and model(s) stored therein) using an API (e.g., acommon API across all applications).

The central intelligence layer includes a number of machine-learnedmodels. For example, as illustrated in FIG. 1C, a respectivemachine-learned model can be provided for each application and managedby the central intelligence layer. In other implementations, two or moreapplications can share a single machine-learned model. For example, insome implementations, the central intelligence layer can provide asingle model for all of the applications. In some implementations, thecentral intelligence layer is included within or otherwise implementedby an operating system of the computing device 50.

The central intelligence layer can communicate with a central devicedata layer. The central device data layer can be a centralizedrepository of data for the computing device 50. As illustrated in FIG.1C, the central device data layer can communicate with a number of othercomponents of the computing device, such as, for example, one or moresensors, a context manager, a device state component, and/or additionalcomponents. In some implementations, the central device data layer cancommunicate with each device component using an API (e.g., a privateAPI).

Example Model Arrangements

FIG. 2B depicts a block diagram of an example machine-learned model 200generated through an architecture search technique to emphasize trainingspeed according to example embodiments of the present disclosure. Insome implementations, the machine-learned model 202 is trained toreceive a set of input data 204 (e.g., image data, statistical data,video data, etc.) and, as a result of receipt of the input data 204,provide output data 210. More particularly, the machine-learned model202 can include a plurality of stages. The machine-learned model 202 caninclude a first model stage 206 and a second model stage 208.

More particularly, the machine-learned model 202 can be generated usinga neural architecture search method that is configured to emphasizetraining speed. As an example, the model can include two stages (e.g.,first model stage 206, second model stage 208, etc.). The first modelstage 206 can include two model layers 206A and 206B (e.g.,convolutional layers, fused convolutional layers, etc.). The secondmodel stage 208 can include three layers 208A-208C.

More particularly, the architecture of the machine-learned model 202 canbe configured to facilitate model training using a progressivelyincreasing regularization magnitude (e.g., a degree in which aregularization technique affects the training of the model 202, etc.).As an example, the machine-learned model 202 can be trained using aninitial regularization magnitude that corresponds to the number oflayers (e.g., 206A, 206B, etc.) included in the first model stage 206.The regularization magnitude can increase to a second magnitude greaterthan the initial magnitude that corresponds to the number of layersincluded in the second model stage 208 (e.g., 208A, 208B, 208C, etc.).In such fashion, the architecture of the model 202 can include aplurality of stages that each include a number of layers corresponds toa particular magnitude of regularization, therefore increasing theefficacy of progressive regularization during training.

FIG. 3 depicts a graphical diagram 300 of an example neural architecturesearch approach to emphasize training speed and accuracy according toexample embodiments of the present disclosure. More particularly, atraining controller 302 can determine a magnitude of regularization tobe implemented at a trainer 304. For example, the controller 302 canprogressively regularize the training process implemented by trainer 304over a number of iterations (e.g., alteration of training data, modeldropouts, etc.). The trainer 304 can train the machine-learned model 306for a number of iterations at the regularization magnitude indicated bythe controller 302.

After an initial number of training iterations, a multi-reward objectivefunction 308 can evaluate an accuracy and training time of themachine-learned model 306 (e.g., reported by trainer 304, etc.). Basedon this evaluation, the multi-reward objective function 308 can providefeedback to the controller 302, which can subsequently adjust amagnitude of regularization for additional training iterations of themachine-learned model 306.

As an example, the controller 302 can obtain a plurality of trainingsamples, and can provide the training samples to the trainer 304. Next,the controller 302 can determine an initial magnitude of regularizationfor implementation at the trainer 304 that is configured to control arelative effect of one or more regularization techniques (e.g., modeldropout(s), training data augmentation, etc.). For example, thecontroller may determine a relatively weak magnitude of regularizationfor training, and then progressively increase the regularization overtime. In some implementations, the controller 302 can also determine aninitial complexity of the training samples that corresponds to themagnitude of regularization. As an example, the controller 302 maydetermine a relatively weak level of training sample complexity thatcorresponds to the relatively weak level of regularization. For example,if the training samples include image data, the controller may downscalethe images (e.g., from 800×600 to 80×60, etc.) to reduce the overalllevel of complexity.

The trainer 304 can train the machine-learned model 306 based at leastin part on the regularization magnitude determined by the controller 302for one or more initial training iterations. After the one or moreinitial training iterations, the trainer 304 can provide model accuracydata and training speed data for evaluation using a multi-rewardobjective function 308. In some implementations, the multi-rewardobjective function 308 can be configured to reward both training speedand accuracy such that training speed is increased while maintaining aspecific, threshold level of accuracy.

Based on this evaluation, feedback can be provided to the controller302. Depending on the feedback, the controller 302 can then increase theregularization magnitude progressively at the trainer 304 and thecomplexity of the training samples. The controller 302 can thenprogressively increase regularization and sample complexity implementedby the trainer 304 during model training. As such, by starting withrelatively weak regularization and complexity, the trainer 304 can trainthe machine-learned model 306 substantially faster than would bepossible using a static level of regularization and sample complexity,therefore increasing training speed and substantially decreasingassociated computational resource cost.

FIG. 4 depicts a data flow diagram 400 of an example method forgeneration of machine-learned models with optimized training speed. Moreparticularly, a model search architecture 402 (e.g., a neural searcharchitecture, etc.) can include or otherwise access a defined modelsearch space 402A. The defined model search space 402A can be orotherwise include one or more searchable parameters (e.g., a number oflayer(s), a type of layer(s) (e.g., convolutional layer(s), fusedconvolutional layer(s), fused MB-CONV layer(s), etc.), a learning rate,hyperparameter(s), layer size, channel size, kernel size, etc.). Itshould be broadly understood that the one or more searchable parametersof the defined model search space 402A can dictate or otherwise controlany particular aspect or implementation of a machine-learned model(e.g., 404, 410, etc.).

Based on the defined model search space 402A, the model searcharchitecture 402 can generate a first machine-learned model 404. Thefirst machine-learned model 404 can include one or more values for theone or more searchable parameters. The first machine-learned model 404can then be trained using model training process 406 to obtain firsttraining data. The first training data can describe a first trainingspeed for the training process 406 performed on the firstmachine-learned model 404 (e.g., an amount of time required fortraining, etc.).

The first training data 408 can be provided to the model searcharchitecture 402. Based on the first training data 408, the model searcharchitecture can generate a second machine-learned model 410 from thedefined model search space 402A. The second machine-learned model 410can include one or more values for the one or more respective searchableparameters that are different than the one or more values of the firstmachine-learned model 404. As an example, one value of the firstmachine-learned model 404 for a searchable parameter may dictate that afirst type of layer is used in the first machine-learned model 404(e.g., a standard convolutional layer, etc.). The second machine-learnedmodel can include a value for the same parameter that instead dictates adifferent type of layer be used in the second machine-learned model 410(e.g., a fused convolutional layer, etc.).

The second machine-learned model 410 can be trained using the same modeltraining process 406 to obtain second training data 412. The secondtraining data 412 can describe a second training speed that is fasterthan the first training speed described by the first training data 408.In such fashion, the model search architecture can iteratively generatemachine-learned models with increasingly fast training speeds bysampling the defined model search space 402A, therefore generating amachine-learned model with optimal training speed characteristics (e.g.,second machine-learned model 410, etc.).

FIG. 5 depicts a flow chart diagram of an example method to performaccording to example embodiments of the present disclosure. AlthoughFIG. 5 depicts steps performed in a particular order for purposes ofillustration and discussion, the methods of the present disclosure arenot limited to the particularly illustrated order or arrangement. Thevarious steps of the method 500 can be omitted, rearranged, combined,and/or adapted in various ways without deviating from the scope of thepresent disclosure.

At 502, a computing system can obtain a plurality of training samplesfor a machine-learned model. More particularly, the computing system canobtain a plurality of training samples (e.g., image data, dataset data,etc.) of a certain complexity that can be utilized to train amachine-learned model for one or more tasks.

At 504, the computing system can, for one or more first trainingiterations, train the machine-learned model based at least in part on afirst regularization magnitude. More particularly, for one or more firsttraining iterations, the computing system can train, based at least inpart on a first regularization magnitude configured to control arelative effect of one or more regularization techniques, themachine-learned model using one or more respective first trainingsamples of the plurality of training samples.

At 506, the computing system can, for one or more second trainingiterations, train the machine-learned model based at least in part on asecond regularization magnitude. More particularly, the computing systemcan, for one or more second training iterations, train, based at leastin part on a second regularization magnitude greater than the firstregularization magnitude, the machine-learned model using one or morerespective second training samples of the plurality of training samples.

Additional Disclosure

The technology discussed herein makes reference to servers, databases,software applications, and other computer-based systems, as well asactions taken and information sent to and from such systems. Theinherent flexibility of computer-based systems allows for a greatvariety of possible configurations, combinations, and divisions of tasksand functionality between and among components. For instance, processesdiscussed herein can be implemented using a single device or componentor multiple devices or components working in combination. Databases andapplications can be implemented on a single system or distributed acrossmultiple systems. Distributed components can operate sequentially or inparallel.

While the present subject matter has been described in detail withrespect to various specific example embodiments thereof, each example isprovided by way of explanation, not limitation of the disclosure. Thoseskilled in the art, upon attaining an understanding of the foregoing,can readily produce alterations to, variations of, and equivalents tosuch embodiments. Accordingly, the subject disclosure does not precludeinclusion of such modifications, variations and/or additions to thepresent subject matter as would be readily apparent to one of ordinaryskill in the art. For instance, features illustrated or described aspart of one embodiment can be used with another embodiment to yield astill further embodiment. Thus, it is intended that the presentdisclosure cover such alterations, variations, and equivalents.

What is claimed is:
 1. A computer-implemented method for efficientmachine-learned model training, comprising: obtaining, by a computingsystem comprising one or more computing devices, a plurality of trainingsamples for a machine-learned model; for one or more first trainingiterations: training, by the computing system based at least in part ona first regularization magnitude configured to control a relative effectof one or more regularization techniques, the machine-learned modelusing one or more respective first training samples of the plurality oftraining samples; and for one or more second training iterations:training, by the computing system based at least in part on a secondregularization magnitude greater than the first regularizationmagnitude, the machine-learned model using one or more respective secondtraining samples of the plurality of training samples.
 2. Thecomputer-implemented method of claim 1, wherein: obtaining the pluralityof training samples for a machine-learned model further comprisesdetermining, by the computing system, a first sample complexity for theone or more first training samples; and wherein, prior to training themachine-learned model using the one or more respective second trainingsamples, the method comprises determining, by the computing system, asecond sample complexity for the one or more second training samples,wherein the second sample complexity is greater than the first samplecomplexity.
 3. The computer-implemented method of claim 2, wherein: theplurality of training samples comprises a respective plurality oftraining images; and determining the second sample complexity for theone or more second training samples comprises adjusting, by thecomputing system, a size of one or more second training images, whereinthe size of the one or more second training images is greater than asize of one or more first training images.
 4. The computer-implementedmethod of claim 1, wherein, prior to obtaining the plurality of trainingsamples for the machine-learned model, the method comprises: generating,by the computing system using a machine-learned model searcharchitecture, an initial machine-learned model comprising one or morefirst values for one or more respective parameters; determining, by thecomputing system, a first training speed of the initial machine-learnedmodel; and generating, by the computing system using the machine-learnedmodel search architecture, the machine-learned model, wherein themachine-learned model comprises one or more second values for the one ormore respective parameters, and wherein at least one of the one or moresecond values is different than the one or more first values.
 5. Thecomputer-implemented method of claim 4, wherein method further comprisesdetermining, by the computing system, a second training speed of themachine-learned model, wherein the second training speed is greater thanthe first training speed.
 6. The computer-implemented method of claim 4,wherein the machine-learned model comprises a plurality of sequentialmodel stages, wherein each model stage comprises one or more layers, andwherein a first model stage comprises fewer layers than a second modelstage of the plurality of model stages.
 7. The computer-implementedmethod of claim 1, wherein the one or more regularization techniquescomprise at least one of: adjusting, by the computing system, a numberof model channels of at least one layer of the machine-learned model; oradjusting, by the computing system, at least one characteristic of oneor more training samples of the plurality of training samples.
 8. Thecomputer-implemented method of claim 1, wherein the secondregularization magnitude is based at least in part on one or morerespective training outputs from the one or more first trainingiterations.
 9. A computing system for determination of models withoptimized training speed, comprising: one or more processors; and one ormore tangible, non-transitory computer readable media storingcomputer-readable instructions that when executed by the one or moreprocessors cause the one or more processors to perform operations, theoperations comprising: generating a first machine-learned model from adefined model search space, wherein the defined model search spacecomprises one or more searchable parameters, wherein the firstmachine-learned model comprises a one or more first values for the oneor more searchable parameters; performing a model training process onthe first machine-learned model to obtain first training datadescriptive of a first training speed; generating a secondmachine-learned model from the defined model search space based at leastin part on the first training data, wherein the second machine-learnedmodel comprises one or more second values for the one or more searchableparameters, wherein at least one of the one or more second values isdifferent than the one or more first values; and performing the modeltraining process on the second machine-learned model to obtain secondtraining data descriptive of a second training speed, wherein the secondtraining speed is faster than the first training speed.
 10. Thecomputing system of claim 9, wherein plurality of model layers of thedefined model search space comprises at least one of: a convolutionallayer; or a fused convolutional layer.
 11. The computing system of claim9, wherein the second machine-learned model comprises a plurality ofsequential model stages, wherein each model stage comprises one or moremodel layers, and wherein a first model stage comprises fewer modellayers than a second model stage of the plurality of model stages. 12.The computing system of claim 9, wherein the first training data isfurther descriptive of a first model accuracy, and wherein the secondtraining data is further descriptive of a second training accuracygreater than the first training accuracy.
 13. The computing system ofclaim 12, wherein generating the second machine-learned model from thedefined model search space is further based at least in part on thefirst training accuracy.
 14. The computing system of claim 9, whereinperforming a model training process on the first machine-learned modelcomprises: obtaining a plurality of training samples for the firstmachine-learned model; for one or more first training iterations:training, based at least in part on a first regularization magnitudeconfigured to control a relative effect of one or more regularizationtechniques, the first machine-learned model using one or more respectivefirst training samples of the plurality of training samples; and for oneor more second training iterations: training, based at least in part ona second regularization magnitude greater than the first regularizationmagnitude, the first machine-learned model using one or more respectivesecond training samples of the plurality of training samples.
 15. Thecomputing system of claim 14, wherein the plurality of training samplescomprises a respective plurality of training images; and whereindetermining the second sample complexity for the one or more secondtraining samples comprises adjusting a size of one or more secondtraining images, wherein the size of the one or more second trainingimages is greater than a size of one or more first training images. 16.The computing system of claim 15, wherein the plurality of trainingsamples comprises a respective plurality of training images; anddetermining the second sample complexity for the one or more secondtraining samples comprises adjusting a size of one or more secondtraining images, wherein the size of the one or more second trainingimages is greater than a size of one or more first training images. 17.The computing system of claim 9, wherein at least one of the one or moreparameters is configured to control one or more of a type of model layeror a number of model layers included in a machine-learned model.
 18. Thecomputing system of claim 9, wherein the operations further compriseproviding the second machine-learned model as an output.
 19. Thecomputing system of claim 9, wherein generating the secondmachine-learned model from the defined model search space furthercomprises determining a plurality of sequential processing stages forthe second machine-learned model, wherein each of the plurality ofsequential processing stages is associated with one or more modellayers, and wherein a number of model layers associated with a secondprocessing stage of the plurality of processing stages is greater anumber of model layers associated with a first processing stage of theplurality of processing stages.